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Getting Started with Performance Analysis of Docker

Docker introduces some intriguing usability, packaging and deployment patterns. These new patterns offer the potential to effect massive improvements to the enterprise application development and operations specialties. Containers also offer the promise of bare metal performance while offering some amount of isolation as well. But can they deliver on that promise ?

Since the early part of January, the Performance Engineering Group at Red Hat has run huge amounts of microbenchmarks, benchmarks and application workloads in Docker containers. The output of that effort has been a steady stream of lessons learned and advice/guidance given to our product architects and developers. How dense can we go ? How fast can it go ? Are these defaults “sane” ? What NOT to do…etc.

Disclaimer: as anyone who has worked with Docker knows, it’s a project under heavy development. I mention that because this blog post includes code snippets and observations that are tied to specific experiments and Docker/kernel versions. YMMV, the answer of course is “it depends”, and so on.

Performance tests we’ve pointed at Docker containers

We’ve done a whole bunch of R&D testing with bleeding edge, “niche” hardware and software to push and pull Docker containers in completely unnatural ways. Based on our choice of benchmarks, you can see that the initial approach was to calculate the precise overhead of containers as compared to bare metal (Red Hat’s Project Atomic will support bare metal deployment of containers). Of course we are also gathering numbers with VMs to compare and containers in VMs (which might be the end-game, who knows…) via OpenStack etc.

Starting at the core, and working our way to the heaviest, pushing all the relevant subsystems to their limits:

Using OpenvSwitch to get past the spanning-tree limitations of # of ports per bridged-interface.

All of these mine-sweeping experiments (lots more to come!) have allowed us to find and fix plenty of issues and document best-practices that we hope will lead to a great customer experience.

BTW if you’re interested in serious, low level, Enterprise-grade performance analysis and tuning for Linux containers (or in general!), let’s have a chat @DockerCon … I’ll be one of the guys in a Project Atomic T-shirt

Unique Docker Philosophies

Ease of use: Docker automates the use of existing Linux kernel technologies into an easily consumable format. Setup and administration of traditionally disjoint subsystems (cgroups, namespaces, iptables, selinux) are encapsulated by Docker.

Packaging: Docker specifies an image/packaging format that allows an application to be packaged with it’s full userspace requirements. No longer is there a necessary interaction between system-level packages (other than the kernel) with the containerized application. The application sees only what is provided inside the container. This can be for example, a specific version of gcc or php that differs from what the host OS provides. I keep drawing an analogy to BIND “views”.

Performance interests aside, those are the 2 main selling points for me, and the benefits of those cannot be overstated.

Surprise, we added some enterprise-y stuff

Docker learns about systemd

Red Hat has taught Docker to use systemd, rather than sysvinit. I mention this because (depending on who you’re talking to) it may be controversial. But I believe that the true promise of containers on Linux relies on specific capabilities that systemd provides: at least init dbus messaging, remote capabilities, cgroups API, remote journaling.

Docker systemd unit-file override:

systemd supports “.d”-style overrides for installed unit-files. This is the correct way to customize the defaults for any systemd unit-file. Overrides go in /etc/systemd/system/.

I need an override for my testing, because I want to use my own bridge device and I want to play with the MTU as well. By default, Docker creates a bridge called docker0 and assigns IP addresses from that pool, useful for development, not production. For production, I guess folks will want to set up their own bridge (or pass through a device, macvlan, whatever).

Assuming you have a bridge that you want to use, create a new systemd unit override file called /etc/systemd/system/docker.service. Here is an example where I’ve set Docker to use a bridge named ‘br1’ and I also added ‘-D’ to enable debug logging for the Docker daemon. br1 is on my test network, on an IP range that I control. Finally, I’ve bumped the MTU to 9000 for some throughput tests…

Also Stephen Tweedie spotted unnecessary memory consumption in systemd mount/umount handling, which was fixed in record time by Lennart Poettering

Docker learns about SELinux

Red Hat has brought SELinux support to Docker. If you’ve been using Red Hat products for any length of time, you know security is a first order concern for us. Look at the stats for critical CVE reponse time…adding SELinux support to Docker should come as no surprise Shout out to the wizards in Red Hat’s Security Response Team, btw.

After the initial bring-up, SELinux support has been fairly painless for us in the Performance Group. Dan Walsh is doing a talk called “SELinux and Docker” at DockerCon next week (June 10, 2pm, actually). To give you a sense of how serious Red Hat is about containers and Docker, I should also mention Red Hat’s CTO Brian Stevens is doing one of the keynotes and we’re Platinum sponsoring. Here’s the very high level picture:

Dockerfile for Performance Analysis

What is a Dockerfile?

Conceptually, a Dockerfile is like a kickstart file for Docker containers. It includes the precise recipe by which Docker builds your container.

Why create a Dockerfile specifically for Performance Analysis?

One of the core principals of Docker images is that they are absolutely as small as possible. This is because when a user wants to use your container image, they must pull it over the network. Docker hosts a registry at http://index.docker.io. Folks may stand up their own internal registries as well, where bandwidth is a bit less of a concern, images can contain site-specific customizations, intellectual property, licensed software, etc.

Our engineers have been working hard to reduce the base image size. Therefore, the base images include the smallest usable package set, plus necessary tooling/package management utilities (yum) to pull in anything else the user needs inside their containers. Think @core on steriods.

Because of the size constraints on the base image, we have to layer on our usual set of Performance Analysis tools via Dockerfile rather than kickstart.

A very common question I get from the field is to provide a precise list of performance analysis packages/tools that I would recommend in their base RHEL images. So I put a slide in the Summit deck this year:

Example Dockerfile

It’s not all that complicated, but includes lots of helpful utilities for characterizing workloads running inside containers. You might see that sysstat is missing; that’s because I monitor that information on the host. This is one critical differentiation between virtualization, and containers: the VCPUs of a KVM guest exist as processes in the host. With containers, the actual containerized binary shows up in the process list of the host. Note: the PID namespace ensures isolation of process tables between containers.

You might also notice that I’m installing numactl and hwloc. That’s because recent versions of Docker provide access to sysfs hardware topology tables from the host, allowing you to apply similar tuning techniques as you would on bare metal on containerized processes. We had some pretty funny test automation explosions when sysfs hardware topology was not exposed Side note, you can’t tune IRQ affinity from a non-privileged container, but luckily IRQ balance really does a great job these days (even knows about PCI-locality). Privileged containers CAN program IRQ affinity.

CPU and memory affinity is another important differentiation between VMs and containers. In a container, core1 is core1 on the host, core2 is core2 etc (depending on your cgroups config). With VMs you apply specific vcpupin/numatune/emulatorpin commands in order to ensure VCPU threads and their memory utilize specific CPUs/memory banks. The process of properly applying affinity to KVM guests is well-documented in Red Hat’s Virtualization Tuning and Optimization Guide. Naturally, when we characterize VMs and containers inside VMs, we often apply much of that.

How to build a container with the Performance Dockerfile

How do I add my benchmark/tool/workload to this Docker container?

Ideally, a pre-configured set of scripts would be committed to your own git repo, and pulled into this container automatically in the Dockerfile (RUN git clone http:///whatever/project.git). This is our approach.

Add a RUN command to the Dockerfile that uses yum, wget, git or similar to pull in, install and configure your software.

Run a container interactively, then pull down the benchmark manually. This is our fallback for some of the more challenging/complex benchmarks and under-load analysis.

How to get a benchmark running inside a Docker container

Let’s take for example, sysbench.

I’ve built RPMs for sysbench for RHEL6 and RHEL7 and committed them to our git repository. I’ve also committed my driver script called run-sysbench.sh. (this isn’t mandatory, but using git makes things a LOT easier).

You can add a RUN statement to the Dockerfile that wget’s your benchmark/tarball from somewhere, or a RUN that does another git clone of some other repository.

However you would normally transfer your code to a new machine, you can do the same thing in the Dockerfile.

Once the container build is complete, launch a container, and kick off your workload. run-sysbench.sh could be any driver/wrapper script that you’ve got.

That’s it. When the script finishes and you’ve copied off the results (part of run-sysbench.sh), you can ‘exit’ the container.

Astute observers will have noticed that I snuck ‘–privileged’ onto the command line above. That is because my run-sysbench.sh wants to drop_caches, and that’s not something permitted to a container by default. As an alternative, instead of using privileges, a container could ssh into it’s host machine as root and drop_cache from there. See Docker source capabilitiesdaemon/execdriver/lxc/init.go for the additional capabilities afforded to “privileged” containers.

It’s not very often that a new technology comes up that creates a whole new column for performance characterization. But containers have done just that, and so it’s been quite the undertaking. There are still many tests variations to run, but so far we’re encouraged.

That said, I have to keep reminding myself that performance isn’t always the first concern for everyone (*gasp*). The packaging, development and deployment workflow that breaks the ties between host userspace and container userspace has frankly been a breath of fresh air.